#725

Committed 21 Oct 2025 07:59AM UTC coverage: 75.387% (-2.9%) from 78.302%

Build # #725

Build Type

Pull #113

github

Committed by

sceredi

Commit Message

chore: generalize action proposals to dynamic entity

Pull Request Pull Request #113: feat: create reinforcement learning controller

Run Details

84 of 179 new or added lines in 20 files covered. (46.93%)

7 existing lines in 4 files now uncovered.

1461 of 1938 relevant lines covered (75.39%)

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75.76

/src/main/scala/io/github/srs/controller/RLControllerModule.scala

package io.github.srs.controller

import cats.effect.unsafe.implicits.global
import io.github.srs.model.ModelModule
import io.github.srs.config.SimulationConfig
import io.github.srs.model.environment.ValidEnvironment
import io.github.srs.utils.random.RNG
import io.github.srs.model.entity.dynamicentity.action.Action
import cats.Id
import io.github.srs.model.Simulation.simulation
import io.github.srs.model.logic.RLLogicsBundle
import io.github.srs.model.entity.dynamicentity.sensor.SensorReadings
import io.github.srs.model.entity.dynamicentity.sensor.Sensor.senseAll
import io.github.srs.view.rendering.EnvironmentRenderer
import io.github.srs.model.entity.dynamicentity.agent.Agent

object RLControllerModule:

  type Observations = Map[Agent, SensorReadings]
  type Infos = Map[Agent, String]

  /**
   * The resonse after each simulation step.
   */
  case class StepResponse private[RLControllerModule] (
      observations: Observations,
      rewards: Map[Agent, Double],
      terminateds: Map[Agent, Boolean],
      truncateds: Map[Agent, Boolean],
      infos: Infos,
  )

  /**
   * Controller trait defines the interface for a Reinforcement Learning controller.
   */
  trait Controller[S <: ModelModule.BaseState]:

    /**
     * The type of the image used for rendering the simulation on the RL client.
     */
    type Image = Array[Byte]

    /**
     * The initial state of the controller.
     */
    def initialState: S

    /**
     * The current state of the controller.
     */
    def state: S

    /**
     * Initializes the controller with the given simulation configuration.
     *
     * @param config
     *   the simulation configuration to initialize the controller.
     */
    def init(config: SimulationConfig[ValidEnvironment]): Unit

    /**
     * Resets the controller to its initial state using the provided random number generator.
     *
     * @param rng
     *   the random number generator to use for reproducibility in the next run.
     */
    def reset(rng: RNG): (Observations, Infos)

    /**
     * Performs a simulation step using the provided actions for each agent.
     *
     * @param actions
     *   a map of agents to their corresponding actions to be performed in this step.
     * @return
     *   a response containing the results of the simulation step.
     */
    def step(actions: Map[Agent, Action[Id]]): StepResponse

    /**
     * Renders the current state of the simulation to an image for the RL client.
     *
     * @param width
     *   the width of the rendered image.
     * @param height
     *   the height of the rendered image.
     *
     * @return
     *   an image representing the current state of the simulation.
     */
    def render(width: Int, height: Int): Image
  end Controller

  trait Provider[S <: ModelModule.BaseState]:
    val controller: Controller[S]

  type Requirements[S <: ModelModule.BaseState] = ModelModule.Provider[S]

  trait Component[S <: ModelModule.BaseState]:
    context: Requirements[S] =>

    object Controller:
      def apply()(using bundle: RLLogicsBundle[S]): Controller[S] = new ControllerImpl

      private class ControllerImpl(using bundle: RLLogicsBundle[S]) extends Controller[S]:

        var _initialState: S =
          bundle.stateLogic.createState(SimulationConfig(simulation, ValidEnvironment.empty))

        var _state: S = initialState

        override def initialState: S = _initialState

        override def state: S = _state

        override def init(config: SimulationConfig[ValidEnvironment]): Unit =
          _initialState = bundle.stateLogic.createState(config)
          _state = _initialState

        override def reset(rng: RNG): (Observations, Infos) =
          _state = context.model.update(state)(using _ => bundle.stateLogic.updateState(initialState, rng))
          state.environment.entities.collect { case a: Agent =>
            (a, a.senseAll[Id](state.environment), "")
          }.map { case (agent, readings, info) =>
            (agent -> readings, agent -> info)
          }.unzip match
            case (obsList, infosList) => (obsList.toMap, infosList.toMap)

        override def step(actions: Map[Agent, Action[Id]]): StepResponse =
          _state = context.model.update(state)(using s => bundle.tickLogic.tick(s, state.dt)).unsafeRunSync()
          StepResponse(
            observations = Map.empty,
            rewards = Map.empty,
            terminateds = Map.empty,
            truncateds = Map.empty,
            infos = Map.empty,
          )

        override def render(width: Int, height: Int): Image =
          EnvironmentRenderer.renderToPNG(state.environment, width, height)
      end ControllerImpl
    end Controller
  end Component

  trait Interface[S <: ModelModule.BaseState] extends Provider[S] with Component[S]:
    self: Requirements[S] =>
end RLControllerModule

1	package io.github.srs.controller
2
3	import cats.effect.unsafe.implicits.global
4	import io.github.srs.model.ModelModule
5	import io.github.srs.config.SimulationConfig
6	import io.github.srs.model.environment.ValidEnvironment
7	import io.github.srs.utils.random.RNG
8	import io.github.srs.model.entity.dynamicentity.action.Action
9	import cats.Id
10	import io.github.srs.model.Simulation.simulation
11	import io.github.srs.model.logic.RLLogicsBundle
12	import io.github.srs.model.entity.dynamicentity.sensor.SensorReadings
13	import io.github.srs.model.entity.dynamicentity.sensor.Sensor.senseAll
14	import io.github.srs.view.rendering.EnvironmentRenderer
15	import io.github.srs.model.entity.dynamicentity.agent.Agent
16
17	object RLControllerModule:
NEW 18		×
19	type Observations = Map[Agent, SensorReadings]
NEW 20	type Infos = Map[Agent, String]	×
21
22	/**
23	* The resonse after each simulation step.
24	*/
25	case class StepResponse private[RLControllerModule] (
26	observations: Observations,	35✔
NEW 27	rewards: Map[Agent, Double],	×
NEW 28	terminateds: Map[Agent, Boolean],	×
NEW 29	truncateds: Map[Agent, Boolean],	×
NEW 30	infos: Infos,	×
NEW 31	)	×
32
33	/**
34	* Controller trait defines the interface for a Reinforcement Learning controller.
35	*/
36	trait Controller[S <: ModelModule.BaseState]:
37
38	/**
39	* The type of the image used for rendering the simulation on the RL client.
40	*/
41	type Image = Array[Byte]
42
43	/**
44	* The initial state of the controller.
45	*/
46	def initialState: S
47
48	/**
49	* The current state of the controller.
50	*/
51	def state: S
52
53	/**
54	* Initializes the controller with the given simulation configuration.
55	*
56	* @param config
57	* the simulation configuration to initialize the controller.
58	*/
59	def init(config: SimulationConfig[ValidEnvironment]): Unit
60
61	/**
62	* Resets the controller to its initial state using the provided random number generator.
63	*
64	* @param rng
65	* the random number generator to use for reproducibility in the next run.
66	*/
67	def reset(rng: RNG): (Observations, Infos)
68
69	/**
70	* Performs a simulation step using the provided actions for each agent.
71	*
72	* @param actions
73	* a map of agents to their corresponding actions to be performed in this step.
74	* @return
75	* a response containing the results of the simulation step.
76	*/
77	def step(actions: Map[Agent, Action[Id]]): StepResponse
78
79	/**
80	* Renders the current state of the simulation to an image for the RL client.
81	*
82	* @param width
83	* the width of the rendered image.
84	* @param height
85	* the height of the rendered image.
86	*
87	* @return
88	* an image representing the current state of the simulation.
89	*/
90	def render(width: Int, height: Int): Image
91	end Controller
92
93	trait Provider[S <: ModelModule.BaseState]:
94	val controller: Controller[S]
95
96	type Requirements[S <: ModelModule.BaseState] = ModelModule.Provider[S]
97
98	trait Component[S <: ModelModule.BaseState]:
NEW 99	context: Requirements[S] =>	×
100
101	object Controller:
102	def apply()(using bundle: RLLogicsBundle[S]): Controller[S] = new ControllerImpl	5✔
103		9✔
104	private class ControllerImpl(using bundle: RLLogicsBundle[S]) extends Controller[S]:
105		8✔
106	var _initialState: S =
107	bundle.stateLogic.createState(SimulationConfig(simulation, ValidEnvironment.empty))	4✔
108		14✔
109	var _state: S = initialState
110		12✔
111	override def initialState: S = _initialState
112		3✔
113	override def state: S = _state
114		3✔
115	override def init(config: SimulationConfig[ValidEnvironment]): Unit =
116	_initialState = bundle.stateLogic.createState(config)
117	_state = _initialState	7✔
118		5✔
119	override def reset(rng: RNG): (Observations, Infos) =
120	_state = context.model.update(state)(using _ => bundle.stateLogic.updateState(initialState, rng))
121	state.environment.entities.collect { case a: Agent =>	13✔
122	(a, a.senseAll[Id](state.environment), "")	17✔
123	}.map { case (agent, readings, info) =>	9✔
124	(agent -> readings, agent -> info)	2✔
125	}.unzip match
126	case (obsList, infosList) => (obsList.toMap, infosList.toMap)	4✔
127		19✔
128	override def step(actions: Map[Agent, Action[Id]]): StepResponse =
129	_state = context.model.update(state)(using s => bundle.tickLogic.tick(s, state.dt)).unsafeRunSync()
130	StepResponse(	16✔
131	observations = Map.empty,	1✔
132	rewards = Map.empty,	3✔
133	terminateds = Map.empty,	3✔
134	truncateds = Map.empty,	3✔
135	infos = Map.empty,	3✔
136	)	5✔
137
138	override def render(width: Int, height: Int): Image =
139	EnvironmentRenderer.renderToPNG(state.environment, width, height)
140	end ControllerImpl	2✔
141	end Controller
142	end Component
143
144	trait Interface[S <: ModelModule.BaseState] extends Provider[S] with Component[S]:
145	self: Requirements[S] =>
146	end RLControllerModule

Scala-Robotics-Simulator / PPS-22-srs / #725

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